import glob
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
data_files = sorted(glob.glob("../data/*"))
data_files
['../data/Folsom_NAM_lat38.579454_lon-121.260320.csv', '../data/Folsom_NAM_lat38.599891_lon-121.126680.csv', '../data/Folsom_NAM_lat38.683880_lon-121.286556.csv', '../data/Folsom_NAM_lat38.704328_lon-121.152788.csv', '../data/Folsom_irradiance.csv', '../data/Folsom_satellite.csv', '../data/Folsom_sky_image_features.csv', '../data/Folsom_weather.csv', '../data/Irradiance_features_day-ahead.csv', '../data/Irradiance_features_intra-day.csv', '../data/Irradiance_features_intra-hour.csv', '../data/NAM_nearest_node_day-ahead.csv', '../data/Sat_image_features_intra-day.csv', '../data/Sky_image_features_intra-hour.csv', '../data/Target_day-ahead.csv', '../data/Target_intra-day.csv', '../data/Target_intra-hour.csv']
target = pd.read_csv("../data/Folsom_irradiance.csv")
target.describe().apply(lambda s: s.apply("{0:.5f}".format))
| ghi | dni | dhi | |
|---|---|---|---|
| count | 1552320.00000 | 1552320.00000 | 1551702.00000 |
| mean | 208.54445 | 259.86476 | 54.26736 |
| std | 295.33004 | 363.15687 | 84.32237 |
| min | 0.00000 | 0.00000 | 0.00000 |
| 25% | 0.00000 | 0.00000 | 0.00000 |
| 50% | 3.23000 | 0.00000 | 3.79550 |
| 75% | 386.90000 | 662.80000 | 79.56000 |
| max | 1466.00000 | 1004.00000 | 748.10000 |
target.info()
<class 'pandas.core.frame.DataFrame'> RangeIndex: 1552320 entries, 0 to 1552319 Data columns (total 4 columns): # Column Non-Null Count Dtype --- ------ -------------- ----- 0 timeStamp 1552320 non-null object 1 ghi 1552320 non-null float64 2 dni 1552320 non-null float64 3 dhi 1551702 non-null float64 dtypes: float64(3), object(1) memory usage: 47.4+ MB
target.head()
| timeStamp | ghi | dni | dhi | |
|---|---|---|---|---|
| 0 | 2014-01-02 08:00:00 | 0.0 | 0.0 | 0.0 |
| 1 | 2014-01-02 08:01:00 | 0.0 | 0.0 | 0.0 |
| 2 | 2014-01-02 08:02:00 | 0.0 | 0.0 | 0.0 |
| 3 | 2014-01-02 08:03:00 | 0.0 | 0.0 | 0.0 |
| 4 | 2014-01-02 08:04:00 | 0.0 | 0.0 | 0.0 |
The term solar irradiance represents the power from the sun that reaches a surface per unit area. Direct irradiance is the part of the solar irradiance that directly reaches a surface; diffuse irradiance is the part that is scattered by the atmosphere; global irradiance is the sum of both diffuse and direct components reaching the same surface.
On the other hand, the term solar irradiation represents the sum of energy per unit area received from the sun over a specific period of time. In the Global Solar Atlas, we provide three magnitudes related to solar irradiation:
GHI, Global Horizontal Irradiation
DNI, Direct Normal Irradiation
DIF, Diffuse Horizontal Irradiation
GHI and DIF are referred to a surface horizontal to the ground, while DNI is referred to a surface perpendicular to the Sun. Higher values of DIF/GHI ratio represent a higher occurrence of clouds, higher atmospheric pollution or higher water vapor content.
type(target.timeStamp[0])
str
target.timeStamp = pd.to_datetime(target.timeStamp)
target.timeStamp.agg(["min", "max"])
min 2014-01-02 08:00:00 max 2016-12-31 07:59:00 Name: timeStamp, dtype: datetime64[ns]
# the brief said a good model was obtained with image, lets look
# at the features generated from said images
image_processed = pd.read_csv("../data/Folsom_sky_image_features.csv")
image_processed.head()
| timestamp | AVG(R) | STD(R) | ENT(R) | AVG(G) | STD(G) | ENT(G) | AVG(B) | STD(B) | ENT(B) | AVG(RB) | STD(RB) | ENT(RB) | AVG(NRB) | STD(NRB) | ENT(NRB) | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 2014-01-01 00:00:11 | 102.8933 | 45.8083 | 5.6373 | 121.5468 | 39.5426 | 5.6390 | 133.4322 | 30.8409 | 5.4275 | 0.7457 | 0.1647 | 4.8206 | -0.1554 | 0.1033 | 4.2279 |
| 1 | 2014-01-01 00:01:10 | 109.1193 | 44.9516 | 5.6762 | 128.0568 | 38.7453 | 5.6535 | 139.4049 | 30.2546 | 5.4146 | 0.7601 | 0.1547 | 4.7120 | -0.1447 | 0.0953 | 4.0961 |
| 2 | 2014-01-01 00:02:10 | 118.4310 | 44.4158 | 5.6386 | 129.2313 | 39.1756 | 5.6381 | 134.9957 | 30.8004 | 5.4368 | 0.8591 | 0.1453 | 4.7299 | -0.0822 | 0.0821 | 3.9662 |
| 3 | 2014-01-01 00:03:11 | 108.0799 | 46.3934 | 5.6447 | 129.5778 | 39.5050 | 5.6392 | 142.6288 | 30.9485 | 5.4369 | 0.7347 | 0.1625 | 4.8007 | -0.1626 | 0.1034 | 4.2324 |
| 4 | 2014-01-01 00:04:11 | 106.7813 | 45.5549 | 5.6539 | 126.3533 | 39.0664 | 5.6438 | 137.2983 | 30.4596 | 5.4168 | 0.7541 | 0.1613 | 4.7906 | -0.1494 | 0.1003 | 4.1823 |
image_processed.info()
<class 'pandas.core.frame.DataFrame'> RangeIndex: 775916 entries, 0 to 775915 Data columns (total 16 columns): # Column Non-Null Count Dtype --- ------ -------------- ----- 0 timestamp 775916 non-null object 1 AVG(R) 775916 non-null float64 2 STD(R) 775916 non-null float64 3 ENT(R) 775916 non-null float64 4 AVG(G) 775916 non-null float64 5 STD(G) 775916 non-null float64 6 ENT(G) 775916 non-null float64 7 AVG(B) 775916 non-null float64 8 STD(B) 775916 non-null float64 9 ENT(B) 775916 non-null float64 10 AVG(RB) 775916 non-null float64 11 STD(RB) 775916 non-null float64 12 ENT(RB) 775916 non-null float64 13 AVG(NRB) 775916 non-null float64 14 STD(NRB) 775916 non-null float64 15 ENT(NRB) 775916 non-null float64 dtypes: float64(15), object(1) memory usage: 94.7+ MB
image_processed.describe()
| AVG(R) | STD(R) | ENT(R) | AVG(G) | STD(G) | ENT(G) | AVG(B) | STD(B) | ENT(B) | AVG(RB) | STD(RB) | ENT(RB) | AVG(NRB) | STD(NRB) | ENT(NRB) | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| count | 775916.000000 | 775916.000000 | 775916.000000 | 775916.000000 | 775916.000000 | 775916.000000 | 775916.000000 | 775916.000000 | 775916.000000 | 775916.000000 | 775916.000000 | 775916.000000 | 775916.000000 | 775916.000000 | 775916.000000 |
| mean | 137.707012 | 47.618662 | 5.802955 | 141.824441 | 43.323574 | 5.692493 | 147.171596 | 36.249612 | 5.476716 | 0.919222 | 0.138455 | 4.329280 | -0.049773 | 0.073537 | 3.532675 |
| std | 12.999919 | 6.828796 | 0.214288 | 10.191951 | 6.213200 | 0.206817 | 9.908800 | 5.722567 | 0.223504 | 0.081037 | 0.057342 | 0.541403 | 0.050370 | 0.030394 | 0.562946 |
| min | 64.814600 | 17.987500 | 3.820400 | 80.354100 | 15.836900 | 3.712900 | 89.490200 | 14.130800 | 3.638700 | 0.425400 | 0.000000 | -0.000000 | -0.462900 | 0.000000 | -0.000000 |
| 25% | 129.155500 | 43.600375 | 5.652300 | 134.654700 | 39.400200 | 5.553200 | 140.709000 | 32.618000 | 5.338100 | 0.883300 | 0.107700 | 4.073000 | -0.069500 | 0.053200 | 3.192700 |
| 50% | 139.574750 | 47.185800 | 5.765400 | 142.773000 | 43.156250 | 5.644700 | 147.631950 | 36.227600 | 5.449800 | 0.941000 | 0.134500 | 4.459100 | -0.034400 | 0.067800 | 3.595200 |
| 75% | 146.688500 | 51.074500 | 5.936000 | 148.652700 | 46.858300 | 5.811100 | 153.671400 | 39.853400 | 5.592000 | 0.971200 | 0.160300 | 4.703400 | -0.017300 | 0.086500 | 3.932300 |
| max | 189.981700 | 86.451500 | 6.473000 | 189.981700 | 77.633900 | 6.470500 | 200.078600 | 83.527200 | 6.439500 | 2.166300 | 5.016400 | 6.182500 | 0.226300 | 0.323900 | 5.697500 |
# lets check variables for correlation
corr_image_processed = image_processed.corr().abs()
corr_image_processed[corr_image_processed > 0.5][
corr_image_processed < 1
].style.background_gradient(cmap="turbo")
/tmp/ipykernel_229035/2321413705.py:2: FutureWarning: The default value of numeric_only in DataFrame.corr is deprecated. In a future version, it will default to False. Select only valid columns or specify the value of numeric_only to silence this warning. corr_image_processed = image_processed.corr().abs()
| AVG(R) | STD(R) | ENT(R) | AVG(G) | STD(G) | ENT(G) | AVG(B) | STD(B) | ENT(B) | AVG(RB) | STD(RB) | ENT(RB) | AVG(NRB) | STD(NRB) | ENT(NRB) | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| AVG(R) | nan | 0.599443 | nan | 0.917145 | nan | nan | 0.534310 | nan | nan | 0.751218 | nan | 0.676228 | 0.758439 | 0.723061 | 0.764767 |
| STD(R) | 0.599443 | nan | 0.552332 | 0.610294 | 0.954014 | 0.559567 | nan | 0.735437 | nan | nan | nan | nan | nan | 0.685824 | 0.528716 |
| ENT(R) | nan | 0.552332 | nan | nan | 0.571528 | 0.933143 | nan | 0.556546 | 0.719275 | nan | nan | nan | nan | nan | nan |
| AVG(G) | 0.917145 | 0.610294 | nan | nan | 0.562515 | nan | 0.788749 | nan | nan | nan | nan | 0.621855 | nan | 0.562438 | 0.653129 |
| STD(G) | nan | 0.954014 | 0.571528 | 0.562515 | nan | 0.618097 | nan | 0.886050 | 0.557194 | nan | nan | nan | nan | nan | nan |
| ENT(G) | nan | 0.559567 | 0.933143 | nan | 0.618097 | nan | nan | 0.638294 | 0.873744 | nan | nan | nan | nan | nan | nan |
| AVG(B) | 0.534310 | nan | nan | 0.788749 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan |
| STD(B) | nan | 0.735437 | 0.556546 | nan | 0.886050 | 0.638294 | nan | nan | 0.728633 | nan | nan | nan | nan | nan | nan |
| ENT(B) | nan | nan | 0.719275 | nan | 0.557194 | 0.873744 | nan | 0.728633 | nan | nan | nan | nan | nan | nan | nan |
| AVG(RB) | 0.751218 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.531862 | 0.995034 | 0.768661 | 0.688226 |
| STD(RB) | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.635802 | nan | 0.684455 | 0.660575 |
| ENT(RB) | 0.676228 | nan | nan | 0.621855 | nan | nan | nan | nan | nan | 0.531862 | 0.635802 | nan | 0.550362 | 0.760177 | 0.976830 |
| AVG(NRB) | 0.758439 | nan | nan | nan | nan | nan | nan | nan | nan | 0.995034 | nan | 0.550362 | nan | 0.805550 | 0.705588 |
| STD(NRB) | 0.723061 | 0.685824 | nan | 0.562438 | nan | nan | nan | nan | nan | 0.768661 | 0.684455 | 0.760177 | 0.805550 | nan | 0.847389 |
| ENT(NRB) | 0.764767 | 0.528716 | nan | 0.653129 | nan | nan | nan | nan | nan | 0.688226 | 0.660575 | 0.976830 | 0.705588 | 0.847389 | nan |
image_processed.timestamp = pd.to_datetime(image_processed.timestamp)
from ydata_profiling import ProfileReport
# lets try ydata profiling
profile = ProfileReport(image_processed, title="Image Features Report")
profile
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%matplotlib inline
import matplotlib.pyplot as plt
image_processed.timestamp.agg(["min", "max"])
min 2014-01-01 00:00:11 max 2016-12-31 23:59:59 Name: timestamp, dtype: datetime64[ns]
fig, ax = plt.subplots(4, 4, figsize=(16, 8))
ax = ax.flatten()
# quick visual check of variable temporal evolution
for index, column in enumerate(image_processed.columns[1:]):
to_plot = to_plot = image_processed[["timestamp", column]].set_index("timestamp")
to_plot = to_plot.rolling("7d", min_periods=1).median()
ax[index].plot(to_plot.index, to_plot[column], label=column)
ax[index].set_title(column)
plt.tight_layout()
plt.show()
print(target.shape)
for column in target.columns[1:]:
print(column, target[target[column] > 10].shape)
# got this insider tip to filter out when the sun is not out, irradiance < 10
(1552320, 4) ghi (751878, 4) dni (601151, 4) dhi (751918, 4)
target = target[target.dni > 10]
# filtered down dataset
image_processed = image_processed[image_processed.timestamp.isin(target.timeStamp)]
# lets look like at different rolling averages to see what variables might be relevant
rolling_window = "7d"
fig, ax = plt.subplots(5, 3, figsize=(16, 16))
ax = ax.flatten()
to_plot_target = target[["timeStamp", "dni"]].set_index("timeStamp")
to_plot_target = to_plot_target.rolling(rolling_window, min_periods=1).median()
for index, column in enumerate(image_processed.columns[1:]):
to_plot = to_plot = image_processed[["timestamp", column]].set_index("timestamp")
to_plot = to_plot.rolling(rolling_window, min_periods=1).median()
ax[index].plot(to_plot.index, to_plot[column])
ax[index].set_title(column)
ax2 = ax[index].twinx()
ax2.plot(to_plot_target.index, to_plot_target["dni"], c="r")
plt.tight_layout()
plt.show()
rolling_window = "14d"
fig, ax = plt.subplots(5, 3, figsize=(16, 16))
ax = ax.flatten()
to_plot_target = target[["timeStamp", "dni"]].set_index("timeStamp")
to_plot_target = to_plot_target.rolling(rolling_window, min_periods=1).median()
for index, column in enumerate(image_processed.columns[1:]):
to_plot = to_plot = image_processed[["timestamp", column]].set_index("timestamp")
to_plot = to_plot.rolling(rolling_window, min_periods=1).median()
ax[index].plot(to_plot.index, to_plot[column])
ax[index].set_title(column)
ax2 = ax[index].twinx()
ax2.plot(to_plot_target.index, to_plot_target["dni"], c="r")
plt.tight_layout()
plt.show()
# pandas doesn't like using 1M as a month apparently
rolling_window = "30d"
fig, ax = plt.subplots(5, 3, figsize=(16, 16))
ax = ax.flatten()
to_plot_target = target[["timeStamp", "dni"]].set_index("timeStamp")
to_plot_target = to_plot_target.rolling(rolling_window, min_periods=1).median()
for index, column in enumerate(image_processed.columns[1:]):
to_plot = to_plot = image_processed[["timestamp", column]].set_index("timestamp")
to_plot = to_plot.rolling(rolling_window, min_periods=1).median()
ax[index].plot(to_plot.index, to_plot[column])
ax[index].set_title(column)
ax2 = ax[index].twinx()
ax2.plot(to_plot_target.index, to_plot_target["dni"], c="r")
plt.tight_layout()
plt.show()
# 14 and 30 days seem to indicate good variables but less check some more correlations
corr_image_processed = image_processed.corr().abs()
corr_image_processed[corr_image_processed > 0.5][
corr_image_processed < 1
].style.background_gradient(cmap="turbo")
/tmp/ipykernel_229035/2180187986.py:1: FutureWarning: The default value of numeric_only in DataFrame.corr is deprecated. In a future version, it will default to False. Select only valid columns or specify the value of numeric_only to silence this warning. corr_image_processed = image_processed.corr().abs()
| AVG(R) | STD(R) | ENT(R) | AVG(G) | STD(G) | ENT(G) | AVG(B) | STD(B) | ENT(B) | AVG(RB) | STD(RB) | ENT(RB) | AVG(NRB) | STD(NRB) | ENT(NRB) | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| AVG(R) | nan | 0.557396 | nan | 0.891474 | nan | nan | nan | nan | nan | 0.735358 | nan | 0.656040 | 0.744020 | 0.704589 | 0.752347 |
| STD(R) | 0.557396 | nan | 0.542761 | 0.589323 | 0.954356 | 0.575508 | nan | 0.775462 | nan | nan | nan | nan | nan | 0.670178 | nan |
| ENT(R) | nan | 0.542761 | nan | nan | 0.537844 | 0.915924 | nan | nan | 0.653316 | nan | nan | nan | nan | nan | nan |
| AVG(G) | 0.891474 | 0.589323 | nan | nan | 0.573967 | nan | 0.749432 | nan | nan | nan | nan | 0.526276 | nan | 0.501892 | 0.552288 |
| STD(G) | nan | 0.954356 | 0.537844 | 0.573967 | nan | 0.600430 | nan | 0.907346 | 0.587079 | nan | nan | nan | nan | nan | nan |
| ENT(G) | nan | 0.575508 | 0.915924 | nan | 0.600430 | nan | nan | 0.553868 | 0.838980 | nan | nan | nan | nan | nan | nan |
| AVG(B) | nan | nan | nan | 0.749432 | nan | nan | nan | 0.559336 | nan | nan | nan | nan | nan | nan | nan |
| STD(B) | nan | 0.775462 | nan | nan | 0.907346 | 0.553868 | 0.559336 | nan | 0.681482 | nan | nan | nan | nan | nan | nan |
| ENT(B) | nan | nan | 0.653316 | nan | 0.587079 | 0.838980 | nan | 0.681482 | nan | nan | nan | nan | nan | nan | nan |
| AVG(RB) | 0.735358 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.597236 | 0.995707 | 0.776699 | 0.769071 |
| STD(RB) | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.536626 | nan | 0.619384 | 0.564150 |
| ENT(RB) | 0.656040 | nan | nan | 0.526276 | nan | nan | nan | nan | nan | 0.597236 | 0.536626 | nan | 0.608286 | 0.758902 | 0.968171 |
| AVG(NRB) | 0.744020 | nan | nan | nan | nan | nan | nan | nan | nan | 0.995707 | nan | 0.608286 | nan | 0.810085 | 0.779907 |
| STD(NRB) | 0.704589 | 0.670178 | nan | 0.501892 | nan | nan | nan | nan | nan | 0.776699 | 0.619384 | 0.758902 | 0.810085 | nan | 0.853407 |
| ENT(NRB) | 0.752347 | nan | nan | 0.552288 | nan | nan | nan | nan | nan | 0.769071 | 0.564150 | 0.968171 | 0.779907 | 0.853407 | nan |
import seaborn as sns
full_dataset = image_processed.merge(
target[["timeStamp", "dni"]], left_on="timestamp", right_on="timeStamp"
)
full_dataset.corr().abs().style.background_gradient(cmap="turbo")
/tmp/ipykernel_229035/34368115.py:1: FutureWarning: The default value of numeric_only in DataFrame.corr is deprecated. In a future version, it will default to False. Select only valid columns or specify the value of numeric_only to silence this warning. full_dataset.corr().abs().style.background_gradient(cmap="turbo")
| AVG(R) | STD(R) | ENT(R) | AVG(G) | STD(G) | ENT(G) | AVG(B) | STD(B) | ENT(B) | AVG(RB) | STD(RB) | ENT(RB) | AVG(NRB) | STD(NRB) | ENT(NRB) | dni | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| AVG(R) | 1.000000 | 0.557396 | 0.046286 | 0.891474 | 0.438775 | 0.103449 | 0.435795 | 0.216680 | 0.100386 | 0.735358 | 0.401242 | 0.656040 | 0.744020 | 0.704589 | 0.752347 | 0.084858 |
| STD(R) | 0.557396 | 1.000000 | 0.542761 | 0.589323 | 0.954356 | 0.575508 | 0.384117 | 0.775462 | 0.498943 | 0.376251 | 0.346207 | 0.409716 | 0.404396 | 0.670178 | 0.465653 | 0.073997 |
| ENT(R) | 0.046286 | 0.542761 | 1.000000 | 0.066560 | 0.537844 | 0.915924 | 0.020667 | 0.477771 | 0.653316 | 0.098048 | 0.196714 | 0.008575 | 0.115422 | 0.376702 | 0.050410 | 0.247128 |
| AVG(G) | 0.891474 | 0.589323 | 0.066560 | 1.000000 | 0.573967 | 0.130311 | 0.749432 | 0.425575 | 0.165469 | 0.388770 | 0.313099 | 0.526276 | 0.402937 | 0.501892 | 0.552288 | 0.009567 |
| STD(G) | 0.438775 | 0.954356 | 0.537844 | 0.573967 | 1.000000 | 0.600430 | 0.495334 | 0.907346 | 0.587079 | 0.162723 | 0.210376 | 0.220831 | 0.187526 | 0.465143 | 0.252208 | 0.053899 |
| ENT(G) | 0.103449 | 0.575508 | 0.915924 | 0.130311 | 0.600430 | 1.000000 | 0.085571 | 0.553868 | 0.838980 | 0.111455 | 0.171387 | 0.049498 | 0.132157 | 0.367338 | 0.022992 | 0.317266 |
| AVG(B) | 0.435795 | 0.384117 | 0.020667 | 0.749432 | 0.495334 | 0.085571 | 1.000000 | 0.559336 | 0.226979 | 0.276517 | 0.058479 | 0.142444 | 0.256578 | 0.028217 | 0.053516 | 0.090891 |
| STD(B) | 0.216680 | 0.775462 | 0.477771 | 0.425575 | 0.907346 | 0.553868 | 0.559336 | 1.000000 | 0.681482 | 0.137533 | 0.004241 | 0.091623 | 0.118198 | 0.137568 | 0.084520 | 0.075511 |
| ENT(B) | 0.100386 | 0.498943 | 0.653316 | 0.165469 | 0.587079 | 0.838980 | 0.226979 | 0.681482 | 1.000000 | 0.020128 | 0.035087 | 0.193583 | 0.002905 | 0.177293 | 0.130707 | 0.346278 |
| AVG(RB) | 0.735358 | 0.376251 | 0.098048 | 0.388770 | 0.162723 | 0.111455 | 0.276517 | 0.137533 | 0.020128 | 1.000000 | 0.400832 | 0.597236 | 0.995707 | 0.776699 | 0.769071 | 0.043627 |
| STD(RB) | 0.401242 | 0.346207 | 0.196714 | 0.313099 | 0.210376 | 0.171387 | 0.058479 | 0.004241 | 0.035087 | 0.400832 | 1.000000 | 0.536626 | 0.432195 | 0.619384 | 0.564150 | 0.036127 |
| ENT(RB) | 0.656040 | 0.409716 | 0.008575 | 0.526276 | 0.220831 | 0.049498 | 0.142444 | 0.091623 | 0.193583 | 0.597236 | 0.536626 | 1.000000 | 0.608286 | 0.758902 | 0.968171 | 0.083910 |
| AVG(NRB) | 0.744020 | 0.404396 | 0.115422 | 0.402937 | 0.187526 | 0.132157 | 0.256578 | 0.118198 | 0.002905 | 0.995707 | 0.432195 | 0.608286 | 1.000000 | 0.810085 | 0.779907 | 0.056320 |
| STD(NRB) | 0.704589 | 0.670178 | 0.376702 | 0.501892 | 0.465143 | 0.367338 | 0.028217 | 0.137568 | 0.177293 | 0.776699 | 0.619384 | 0.758902 | 0.810085 | 1.000000 | 0.853407 | 0.173919 |
| ENT(NRB) | 0.752347 | 0.465653 | 0.050410 | 0.552288 | 0.252208 | 0.022992 | 0.053516 | 0.084520 | 0.130707 | 0.769071 | 0.564150 | 0.968171 | 0.779907 | 0.853407 | 1.000000 | 0.027137 |
| dni | 0.084858 | 0.073997 | 0.247128 | 0.009567 | 0.053899 | 0.317266 | 0.090891 | 0.075511 | 0.346278 | 0.043627 | 0.036127 | 0.083910 | 0.056320 | 0.173919 | 0.027137 | 1.000000 |
# chosing the 3 most correlated vars, all have the same name to check pairplot
g = sns.PairGrid(full_dataset, vars=["ENT(B)", "ENT(G)", "ENT(R)"])
g.map_diag(sns.kdeplot, color=".2")
g.map_offdiag(sns.scatterplot, hue=full_dataset["dni"], s=5, linewidth=0)
g.axes[1, -1].legend(loc="center left", bbox_to_anchor=(0.7, 0.5))
<matplotlib.legend.Legend at 0x7fe59c88b5e0>
pairplots = sns.PairGrid(
full_dataset.sample(2000),
vars=["AVG(R)", "STD(R)", "ENT(R)", "AVG(G)", "STD(G)", "ENT(G)"],
)
pairplots.map_diag(sns.kdeplot, color=".4")
pairplots.map_offdiag(sns.scatterplot, hue=full_dataset["dni"], s=5, linewidth=0)
pairplots.axes[1, -1].legend(loc="center left", bbox_to_anchor=(0.7, 0.5))
<matplotlib.legend.Legend at 0x7fe59143c2e0>
# No quick easy win, as expected since the problem seemed to be quite hard
After looking at the data we can see there is no very direct correlation, I focused on the dataset that was derived from images so I will attemp at classification using that. If it works it means that images contain good information to actually analyse the problem and should be investigated further.
As instructed in the brief I will use 2014-15 to train and 2016 to test and construct training examples with no future data. Keep in mind I want to predict irradiance for the next half hour in 1 minute increments.
As a start I will use just the values from the current timestamp to predict 30 min in the future, producing more test examples looking at the last X minutes of data might be a good approach.
# construct train data
train_data = full_dataset[full_dataset.timeStamp.dt.year < 2016]
test_data = full_dataset[full_dataset.timeStamp.dt.year >= 2016]
train_data.shape, test_data.shape
((155332, 18), (59639, 18))
# Lest try a multiouput regressor, seems to be a good fit for the problem
import sklearn
from sklearn.linear_model import LinearRegression
train_data.head()
| timestamp | AVG(R) | STD(R) | ENT(R) | AVG(G) | STD(G) | ENT(G) | AVG(B) | STD(B) | ENT(B) | AVG(RB) | STD(RB) | ENT(RB) | AVG(NRB) | STD(NRB) | ENT(NRB) | timeStamp | dni | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 2014-01-17 15:37:00 | 139.7888 | 34.3469 | 5.5304 | 154.1869 | 29.7720 | 5.4339 | 163.7680 | 23.3183 | 5.0968 | 0.8448 | 0.1114 | 4.3251 | -0.0879 | 0.0630 | 3.6009 | 2014-01-17 15:37:00 | 46.88 |
| 1 | 2014-01-17 15:38:00 | 133.1488 | 34.8151 | 5.5435 | 149.2535 | 30.1141 | 5.4508 | 159.8671 | 23.7961 | 5.1428 | 0.8232 | 0.1163 | 4.3841 | -0.1013 | 0.0673 | 3.6742 | 2014-01-17 15:38:00 | 94.20 |
| 2 | 2014-01-17 15:39:00 | 136.0932 | 35.1223 | 5.5585 | 153.1696 | 30.0517 | 5.4555 | 163.9541 | 23.6320 | 5.1200 | 0.8206 | 0.1155 | 4.3722 | -0.1028 | 0.0671 | 3.6560 | 2014-01-17 15:39:00 | 138.80 |
| 3 | 2014-01-17 15:40:00 | 149.9179 | 33.4076 | 5.5482 | 155.7923 | 29.9247 | 5.4337 | 159.0996 | 24.1299 | 5.1478 | 0.9358 | 0.0994 | 4.2024 | -0.0358 | 0.0511 | 3.3432 | 2014-01-17 15:40:00 | 178.10 |
| 4 | 2014-02-19 15:44:00 | 152.3175 | 43.1066 | 6.0047 | 154.2093 | 38.1726 | 5.8385 | 155.1190 | 32.4359 | 5.5685 | 0.9747 | 0.1425 | 4.6965 | -0.0184 | 0.0773 | 3.7903 | 2014-02-19 15:44:00 | 28.88 |
train_X = train_data[
[
"AVG(R)",
"STD(R)",
"ENT(R)",
"AVG(G)",
"STD(G)",
"ENT(G)",
"AVG(B)",
"STD(B)",
"ENT(B)",
"AVG(RB)",
"STD(RB)",
"ENT(RB)",
"AVG(NRB)",
"STD(NRB)",
"ENT(NRB)",
]
]
train_Y = train_data[["timestamp", "dni"]]
train_target = [
train_Y.iloc[i : i + 30]["dni"].values for i in range(len(train_Y) - 30)
]
len(train_target), len(train_X)
(155302, 155332)
# we lose the last 30 minutes of data due to wanting to stack examples
train_X = train_X[:-30]
len(train_target), len(train_X)
(155302, 155302)
from sklearn.linear_model import LinearRegression
first_model = LinearRegression()
first_model.fit(train_X, train_target)
LinearRegression()
from sklearn import metrics
y_predicted = first_model.predict(train_X)
mae = metrics.mean_absolute_error(train_target, y_predicted)
mse = metrics.mean_squared_error(train_target, y_predicted)
r2 = metrics.r2_score(train_target, y_predicted)
print("The model performance for testing set")
print("--------------------------------------")
print("MAE is {}".format(mae))
print("MSE is {}".format(mse))
print("R2 score is {}".format(r2))
The model performance for testing set -------------------------------------- MAE is 151.1144044345185 MSE is 39723.9587803872 R2 score is 0.3829785607982642
# a first attempt, and what a bad one!! R2 of 0.38 is not a good one sadly
# this is looking at the whole 30 min interval at once, could be interesting to dissect it further
# given this performance testing it on the 2016 data seemed pointless
from sklearn.ensemble import RandomForestRegressor
second_model = RandomForestRegressor()
second_model.fit(train_X, train_target)
y_predicted = second_model.predict(train_X)
mae = metrics.mean_absolute_error(train_target, y_predicted)
mse = metrics.mean_squared_error(train_target, y_predicted)
r2 = metrics.r2_score(train_target, y_predicted)
print("The model performance for testing set")
print("--------------------------------------")
print("MAE is {}".format(mae))
print("MSE is {}".format(mse))
print("R2 score is {}".format(r2))
The model performance for testing set -------------------------------------- MAE is 27.934698551531643 MSE is 2380.681520738079 R2 score is 0.9630214252151232
# Very different case with a R2 score that probably indicates severe overfitting
# I will bit and see what it looks like for the 2016 data
# lets build our datasets as we did previously
test_X = test_data[
[
"AVG(R)",
"STD(R)",
"ENT(R)",
"AVG(G)",
"STD(G)",
"ENT(G)",
"AVG(B)",
"STD(B)",
"ENT(B)",
"AVG(RB)",
"STD(RB)",
"ENT(RB)",
"AVG(NRB)",
"STD(NRB)",
"ENT(NRB)",
]
]
test_Y = test_data[["timestamp", "dni"]]
test_target = [
test_Y.iloc[i : i + 30]["dni"].values for i in range(len(test_Y) - 30)
]
len(test_target), len(test_X)
(59609, 59639)
# we lose the last 30 minutes of data due to wanting to stack examples
test_X = test_X[:-30]
len(test_target), len(test_X)
(59609, 59609)
y_predicted = second_model.predict(test_X)
mae = metrics.mean_absolute_error(test_target, y_predicted)
mse = metrics.mean_squared_error(test_target, y_predicted)
r2 = metrics.r2_score(test_target, y_predicted)
print("The model performance for testing set")
print("--------------------------------------")
print("MAE is {}".format(mae))
print("MSE is {}".format(mse))
print("R2 score is {}".format(r2))
The model performance for testing set -------------------------------------- MAE is 152.13548526877938 MSE is 41988.599555154826 R2 score is 0.36701165707100575
# as expected the model is severly overfitting to the training data and not generalising well
# given I had the code why not inspect the first model...
y_predicted = first_model.predict(test_X)
mae = metrics.mean_absolute_error(test_target, y_predicted)
mse = metrics.mean_squared_error(test_target, y_predicted)
r2 = metrics.r2_score(test_target, y_predicted)
print("The model performance for testing set")
print("--------------------------------------")
print("MAE is {}".format(mae))
print("MSE is {}".format(mse))
print("R2 score is {}".format(r2))
The model performance for testing set -------------------------------------- MAE is 158.15315481427191 MSE is 47679.15038543434 R2 score is 0.28122051062756576
# performs even worse than the overfitted model
# first try to solve the overfit is to reduce the number of estimators that defaults to 100
second_model = RandomForestRegressor(n_estimators=10)
second_model.fit(train_X, train_target)
y_predicted = second_model.predict(train_X)
mae = metrics.mean_absolute_error(train_target, y_predicted)
mse = metrics.mean_squared_error(train_target, y_predicted)
r2 = metrics.r2_score(train_target, y_predicted)
print("The model performance for training set")
print("--------------------------------------")
print("MAE is {}".format(mae))
print("MSE is {}".format(mse))
print("R2 score is {}".format(r2))
y_predicted = second_model.predict(test_X)
mae = metrics.mean_absolute_error(test_target, y_predicted)
mse = metrics.mean_squared_error(test_target, y_predicted)
r2 = metrics.r2_score(test_target, y_predicted)
print("The model performance for testing set")
print("--------------------------------------")
print("MAE is {}".format(mae))
print("MSE is {}".format(mse))
print("R2 score is {}".format(r2))
The model performance for training set -------------------------------------- MAE is 31.637146191936097 MSE is 3530.1626718801813 R2 score is 0.9451668117824664 The model performance for testing set -------------------------------------- MAE is 156.95940453678682 MSE is 45579.18583755054 R2 score is 0.31288409342496115
# once again, "incredible" performance in train, not so much in test
# lets also reduce the depth of the trees
second_model = RandomForestRegressor(n_estimators=10, max_depth=20)
second_model.fit(train_X, train_target)
y_predicted = second_model.predict(train_X)
mae = metrics.mean_absolute_error(train_target, y_predicted)
mse = metrics.mean_squared_error(train_target, y_predicted)
r2 = metrics.r2_score(train_target, y_predicted)
print("The model performance for training set")
print("--------------------------------------")
print("MAE is {}".format(mae))
print("MSE is {}".format(mse))
print("R2 score is {}".format(r2))
y_predicted = second_model.predict(test_X)
mae = metrics.mean_absolute_error(test_target, y_predicted)
mse = metrics.mean_squared_error(test_target, y_predicted)
r2 = metrics.r2_score(test_target, y_predicted)
print("The model performance for testing set")
print("--------------------------------------")
print("MAE is {}".format(mae))
print("MSE is {}".format(mse))
print("R2 score is {}".format(r2))
The model performance for training set -------------------------------------- MAE is 58.08567806684584 MSE is 8708.873466662211 R2 score is 0.8647268812563774 The model performance for testing set -------------------------------------- MAE is 150.71641823792714 MSE is 44185.48687279728 R2 score is 0.3338933184699356
# still no silver bullet
# I have had good results with GradientBoosting in the past, perhaps...
from sklearn.ensemble import GradientBoostingRegressor
from sklearn.multioutput import MultiOutputRegressor
third_model = MultiOutputRegressor(GradientBoostingRegressor(verbose=0, n_estimators=20))
third_model.fit(train_X, train_target)
y_predicted = third_model.predict(train_X)
mae = metrics.mean_absolute_error(train_target, y_predicted)
mse = metrics.mean_squared_error(train_target, y_predicted)
r2 = metrics.r2_score(train_target, y_predicted)
print("The model performance for training set")
print("--------------------------------------")
print("MAE is {}".format(mae))
print("MSE is {}".format(mse))
print("R2 score is {}".format(r2))
y_predicted = third_model.predict(test_X)
mae = metrics.mean_absolute_error(test_target, y_predicted)
mse = metrics.mean_squared_error(test_target, y_predicted)
r2 = metrics.r2_score(test_target, y_predicted)
print("The model performance for testing set")
print("--------------------------------------")
print("MAE is {}".format(mae))
print("MSE is {}".format(mse))
print("R2 score is {}".format(r2))
The model performance for training set -------------------------------------- MAE is 162.90189369587523 MSE is 43088.00211632447 R2 score is 0.33072860236354384 The model performance for testing set -------------------------------------- MAE is 170.71213372328984 MSE is 47574.07530654866 R2 score is 0.282831701582572
# still no silver bullet, problem is indeed hard to solve!
# as a last hail mary I will try to wrap the random forest regressor into the multioutput regressor
# this means an estimator per output instead of a general one
from sklearn.ensemble import GradientBoostingRegressor
from sklearn.multioutput import MultiOutputRegressor
fourth_model = MultiOutputRegressor(RandomForestRegressor(verbose=1, n_estimators=10, n_jobs=8))
fourth_model.fit(train_X, train_target)
y_predicted = fourth_model.predict(train_X)
mae = metrics.mean_absolute_error(train_target, y_predicted)
mse = metrics.mean_squared_error(train_target, y_predicted)
r2 = metrics.r2_score(train_target, y_predicted)
print("The model performance for training set")
print("--------------------------------------")
print("MAE is {}".format(mae))
print("MSE is {}".format(mse))
print("R2 score is {}".format(r2))
y_predicted = fourth_model.predict(test_X)
mae = metrics.mean_absolute_error(test_target, y_predicted)
mse = metrics.mean_squared_error(test_target, y_predicted)
r2 = metrics.r2_score(test_target, y_predicted)
print("The model performance for testing set")
print("--------------------------------------")
print("MAE is {}".format(mae))
print("MSE is {}".format(mse))
print("R2 score is {}".format(r2))
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[Parallel(n_jobs=8)]: Done 6 out of 10 | elapsed: 0.1s remaining: 0.1s [Parallel(n_jobs=8)]: Done 10 out of 10 | elapsed: 0.1s finished [Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers. [Parallel(n_jobs=8)]: Done 6 out of 10 | elapsed: 0.1s remaining: 0.1s [Parallel(n_jobs=8)]: Done 10 out of 10 | elapsed: 0.2s finished [Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers. [Parallel(n_jobs=8)]: Done 6 out of 10 | elapsed: 0.1s remaining: 0.1s [Parallel(n_jobs=8)]: Done 10 out of 10 | elapsed: 0.2s finished [Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers. [Parallel(n_jobs=8)]: Done 6 out of 10 | elapsed: 0.1s remaining: 0.1s [Parallel(n_jobs=8)]: Done 10 out of 10 | elapsed: 0.2s finished
The model performance for training set -------------------------------------- MAE is 31.63654642910829 MSE is 3529.971010159968 R2 score is 0.9451697207423221
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers. [Parallel(n_jobs=8)]: Done 6 out of 10 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=8)]: Done 10 out of 10 | elapsed: 0.1s finished [Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers. [Parallel(n_jobs=8)]: Done 6 out of 10 | elapsed: 0.1s remaining: 0.0s [Parallel(n_jobs=8)]: Done 10 out of 10 | elapsed: 0.1s finished [Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers. [Parallel(n_jobs=8)]: Done 6 out of 10 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=8)]: Done 10 out of 10 | elapsed: 0.1s finished [Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers. [Parallel(n_jobs=8)]: Done 6 out of 10 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=8)]: Done 10 out of 10 | elapsed: 0.1s finished [Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers. [Parallel(n_jobs=8)]: Done 6 out of 10 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=8)]: Done 10 out of 10 | elapsed: 0.1s finished [Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers. [Parallel(n_jobs=8)]: Done 6 out of 10 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=8)]: Done 10 out of 10 | elapsed: 0.1s finished [Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers. [Parallel(n_jobs=8)]: Done 6 out of 10 | elapsed: 0.1s remaining: 0.0s [Parallel(n_jobs=8)]: Done 10 out of 10 | elapsed: 0.1s finished [Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers. [Parallel(n_jobs=8)]: Done 6 out of 10 | elapsed: 0.1s remaining: 0.0s [Parallel(n_jobs=8)]: Done 10 out of 10 | elapsed: 0.1s finished [Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers. 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[Parallel(n_jobs=8)]: Done 6 out of 10 | elapsed: 0.1s remaining: 0.0s [Parallel(n_jobs=8)]: Done 10 out of 10 | elapsed: 0.1s finished [Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers. [Parallel(n_jobs=8)]: Done 6 out of 10 | elapsed: 0.1s remaining: 0.0s [Parallel(n_jobs=8)]: Done 10 out of 10 | elapsed: 0.1s finished [Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers. [Parallel(n_jobs=8)]: Done 6 out of 10 | elapsed: 0.1s remaining: 0.0s [Parallel(n_jobs=8)]: Done 10 out of 10 | elapsed: 0.1s finished [Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers. [Parallel(n_jobs=8)]: Done 6 out of 10 | elapsed: 0.1s remaining: 0.0s [Parallel(n_jobs=8)]: Done 10 out of 10 | elapsed: 0.1s finished [Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers. [Parallel(n_jobs=8)]: Done 6 out of 10 | elapsed: 0.1s remaining: 0.0s [Parallel(n_jobs=8)]: Done 10 out of 10 | elapsed: 0.1s finished [Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers. [Parallel(n_jobs=8)]: Done 6 out of 10 | elapsed: 0.1s remaining: 0.0s [Parallel(n_jobs=8)]: Done 10 out of 10 | elapsed: 0.1s finished
The model performance for testing set -------------------------------------- MAE is 157.42195742589183 MSE is 45818.02723249825 R2 score is 0.30928208293217996
from sklearn.ensemble import GradientBoostingRegressor
from sklearn.multioutput import MultiOutputRegressor
fourth_model = MultiOutputRegressor(RandomForestRegressor(verbose=0, n_estimators=5, n_jobs=8))
fourth_model.fit(train_X, train_target)
y_predicted = fourth_model.predict(train_X)
mae = metrics.mean_absolute_error(train_target, y_predicted)
mse = metrics.mean_squared_error(train_target, y_predicted)
r2 = metrics.r2_score(train_target, y_predicted)
print("The model performance for training set")
print("--------------------------------------")
print("MAE is {}".format(mae))
print("MSE is {}".format(mse))
print("R2 score is {}".format(r2))
y_predicted = fourth_model.predict(test_X)
mae = metrics.mean_absolute_error(test_target, y_predicted)
mse = metrics.mean_squared_error(test_target, y_predicted)
r2 = metrics.r2_score(test_target, y_predicted)
print("The model performance for testing set")
print("--------------------------------------")
print("MAE is {}".format(mae))
print("MSE is {}".format(mse))
print("R2 score is {}".format(r2))
The model performance for training set -------------------------------------- MAE is 34.03842106219094 MSE is 4792.456770904482 R2 score is 0.925559791545919 The model performance for testing set -------------------------------------- MAE is 162.79992482455117 MSE is 50019.57938298719 R2 score is 0.24594398044542942
# Nothing still, big train overfit, test not performing
None of the quick classical approaches worked, although some might have shown some promise. It overall seems that predicting general 2weeks+ trends wouldn't be difficult but the situation is much trickier when looking at minute by minute changes.
During my exploration given to the constraint of the time I made a guesstimate on what dataset could have given me the best return for time invested. I got good tips about solar positions + energy that I could have incorporated.
With more time available looking at other datasets and combining them would be next on the list. I would also like to try some image classification methods given my experience with CNNs and my desire to apply transformer based models to image classification.
Although no classifier performed well at all it was a very interesting dataset and exercise to go through and it shows that some problems are much harder to solve than what they seem to be at a glance.
# new attemp, folding in more data, lets say 30 minutes prior
train_historical = [
train_X.iloc[i : i + 30].values.flatten() for i in range(len(train_X) - 30)
]
test_historical = [
test_X.iloc[i : i + 30].values.flatten() for i in range(len(test_X) - 30)
]
len(train_target), len(train_historical)
(155302, 155302)
len(test_target), len(test_historical)
(59609, 59609)
historical_model = RandomForestRegressor(verbose=1, n_estimators=10)
historical_model.fit(train_historical, train_target)
y_predicted = historical_model.predict(train_historical)
mae = metrics.mean_absolute_error(train_target, y_predicted)
mse = metrics.mean_squared_error(train_target, y_predicted)
r2 = metrics.r2_score(train_target, y_predicted)
print("The model performance for training set")
print("--------------------------------------")
print("MAE is {}".format(mae))
print("MSE is {}".format(mse))
print("R2 score is {}".format(r2))
y_predicted = historical_model.predict(test_historical)
mae = metrics.mean_absolute_error(test_target, y_predicted)
mse = metrics.mean_squared_error(test_target, y_predicted)
r2 = metrics.r2_score(test_target, y_predicted)
print("The model performance for testing set")
print("--------------------------------------")
print("MAE is {}".format(mae))
print("MSE is {}".format(mse))
print("R2 score is {}".format(r2))
[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers. [Parallel(n_jobs=1)]: Done 1 out of 1 | elapsed: 2.8min finished [Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers. [Parallel(n_jobs=1)]: Done 1 out of 1 | elapsed: 0.2s finished
The model performance for training set -------------------------------------- MAE is 31.408151944812914 MSE is 10791.431691995518 R2 score is 0.8323812211296473
[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers. [Parallel(n_jobs=1)]: Done 1 out of 1 | elapsed: 0.1s finished
The model performance for testing set -------------------------------------- MAE is 155.23592459192406 MSE is 58485.75992749478 R2 score is 0.11835087431756834
historical_model = RandomForestRegressor(verbose=1, n_estimators=10)
historical_model.fit(train_historical, train_target)
y_predicted = historical_model.predict(train_historical)
mae = metrics.mean_absolute_error(train_target, y_predicted)
mse = metrics.mean_squared_error(train_target, y_predicted)
r2 = metrics.r2_score(train_target, y_predicted)
print("The model performance for training set")
print("--------------------------------------")
print("MAE is {}".format(mae))
print("MSE is {}".format(mse))
print("R2 score is {}".format(r2))
y_predicted = historical_model.predict(test_historical)
mae = metrics.mean_absolute_error(test_target, y_predicted)
mse = metrics.mean_squared_error(test_target, y_predicted)
r2 = metrics.r2_score(test_target, y_predicted)
print("The model performance for testing set")
print("--------------------------------------")
print("MAE is {}".format(mae))
print("MSE is {}".format(mse))
print("R2 score is {}".format(r2))
[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers. [Parallel(n_jobs=1)]: Done 10 out of 10 | elapsed: 28.3min finished [Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers. [Parallel(n_jobs=1)]: Done 10 out of 10 | elapsed: 0.6s finished
The model performance for training set -------------------------------------- MAE is 24.447988412041187 MSE is 2274.6796314844123 R2 score is 0.9646683529827272
[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.
The model performance for testing set -------------------------------------- MAE is 113.90549603862961 MSE is 28571.561076243266 R2 score is 0.5692949208747057
[Parallel(n_jobs=1)]: Done 10 out of 10 | elapsed: 0.3s finished
historical_model = RandomForestRegressor(verbose=1, n_estimators=50, n_jobs=8)
historical_model.fit(train_historical, train_target)
y_predicted = historical_model.predict(train_historical)
mae = metrics.mean_absolute_error(train_target, y_predicted)
mse = metrics.mean_squared_error(train_target, y_predicted)
r2 = metrics.r2_score(train_target, y_predicted)
print("The model performance for training set")
print("--------------------------------------")
print("MAE is {}".format(mae))
print("MSE is {}".format(mse))
print("R2 score is {}".format(r2))
y_predicted = historical_model.predict(test_historical)
mae = metrics.mean_absolute_error(test_target, y_predicted)
mse = metrics.mean_squared_error(test_target, y_predicted)
r2 = metrics.r2_score(test_target, y_predicted)
print("The model performance for testing set")
print("--------------------------------------")
print("MAE is {}".format(mae))
print("MSE is {}".format(mse))
print("R2 score is {}".format(r2))
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers. [Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 21.7min [Parallel(n_jobs=8)]: Done 50 out of 50 | elapsed: 28.9min finished [Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers. [Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.7s [Parallel(n_jobs=8)]: Done 50 out of 50 | elapsed: 1.0s finished
The model performance for training set -------------------------------------- MAE is 21.507125678442254 MSE is 1547.8609599516042 R2 score is 0.9759577168554727
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers. [Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.3s [Parallel(n_jobs=8)]: Done 50 out of 50 | elapsed: 0.4s finished
The model performance for testing set -------------------------------------- MAE is 109.83442868224607 MSE is 26179.579412924006 R2 score is 0.6053531589642825
# this was a good idea, although it took a lot longer to train for quick ideation
# in the feature we need to try: increase number of estimators, going further than 30 mins in the past
# also need to investigate where the errors are instead of just looking at the classifier as a black box